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• W3192941844 abstract "Because of the massive amount of source data, certain data relationships are often hidden in these data, and there are certain trends in the data. For e-commerce, the prediction and estimation of these data trends are particularly important. Providing reasonable forecasts will help companies make effective decisions about the current situation and provide customers with appropriate services, thereby helping companies seize customers to generate more commercial profits. This paper analyzes the shortcomings of the Apriori algorithm and proposes an association rule mining technology to optimize the shortcomings of the Apriori algorithm. In the experiment, the test code was written in the Web environment, and the optimized algorithm and the Apriori algorithm were used for data mining on the same number of itemsets under the same support conditions. Experimental data shows that the improved algorithm has a certain improvement compared to the Apriori algorithm, and the performance is more obvious when the support is low. The experimental results show that when the support is 0.1, 0.2, 0.3, 0.4, 0.5, the improved Apriori algorithm is 12s, 17s, 12s, 8s, and 6s more than the traditional Apriori algorithm. In the case of a high degree of support, the improved algorithm has no obvious advantages. This is because the number of times the algorithm is executed is low, and the auxiliary table participates in the mining algorithm less frequently." @default.
• W3192941844 created "2021-08-16" @default.
• W3192941844 creator A5009408707 @default.
• W3192941844 date "2021-06-28" @default.
• W3192941844 modified "2023-10-16" @default.
• W3192941844 title "Application of Apriori Association Rules Algorithm to Data Mining Technology to Mining E-commerce Potential Customers" @default.
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• W3192941844 doi "https://doi.org/10.1109/iwcmc51323.2021.9498773" @default.
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